Intelligent biohazard training based on real-time task recognition

Virtual environments offer an ideal setting to develop intelligent training applications. Yet, their ability to support complex procedures depends on the appropriate integration of knowledge-based techniques and natural interaction. In this article, we describe the implementation of an intelligent rehearsal system for biohazard laboratory procedures, based on the real-time instantiation of task models from the trainee’s actions. A virtual biohazard laboratory has been recreated using the Unity3D engine, in which users interact with laboratory objects using keyboard/mouse input or hand gestures through a Kinect device. Realistic behavior for objects is supported by the implementation of a relevant subset of common sense and physics knowledge. User interaction with objects leads to the recognition of specific actions, which are used to progressively instantiate a task-based representation of biohazard procedures. The dynamics of this instantiation process supports trainee evaluation as well as real-time assistance. This system is designed primarily as a rehearsal system providing real-time advice and supporting user performance evaluation. We provide detailed examples illustrating error detection and recovery, and results from on-site testing with students from the Faculty of Medical Sciences at Kyushu University. In the study, we investigate the usability aspect by comparing interaction with mouse and Kinect devices and the effect of real-time task recognition on recovery time after user mistakes

Narrative balance management in an Intelligent biosafety training application for improving user performance

The use of three-dimensional virtual environments in training applications supports the simulation of complex scenarios and realistic object behaviour. While these environments have the potential to provide an advanced training experience to students, it is difficult to design and manage a training session in real time due to the number of parameters to pay attention to: timing of events, difficulty, user’s actions and their consequences or eventualities are some examples. For that purpose, we have extended our virtual Bio-safety Laboratory application used for training biohazard procedures with a Narrative Manager. The Narrative Manager controls the simulation deciding which events will take place in the simulation, and when, by controlling the narrative balance of the session. Our hypothesis is that the Narrative Manager allows us to increase the number of tasks for the user to solve and, due to balancing difficulty and intensity, it keeps the user interested in training. When evaluating our system we observed that the Narrative Manager effectively introduces more tasks for the user to solve, and despite that, is accepted by the users as more interesting and not harder than an identical system without a Narrative Manager. Also, a knowledge test demonstrated better results in users’ interest and learning output in the narrative condition

Mars Sample Handling Protocol Workshop Series: Workshop 4

In preparation for missions to Mars that will involve the return of samples to Earth, it will be necessary to prepare for the receiving, handling, testing, distributing, and archiving of martian materials here on Earth. Previous groups and committees have studied selected aspects of sample return activities, but specific detailed protocols for the handling and testing of returned samples must still be developed. To further refine the requirements for sample hazard testing and to develop the criteria for subsequent release of sample materials from quarantine, the NASA Planetary Protection Officer convened a series of workshops in 2000-2001. The overall objective of the Workshop Series was to produce a Draft Protocol by which returned martian sample materials can be assessed for biological hazards and examined for evidence of life (extant or extinct) while safeguarding the purity of the samples from possible terrestrial contamination. This report also provides a record of the proceedings of Workshop 4, the final Workshop of the Series, which was held in Arlington, Virginia, June 5-7, 2001. During Workshop 4, the sub-groups were provided with a draft of the protocol compiled in May 2001 from the work done at prior Workshops in the Series. Then eight sub-groups were formed to discuss the following assigned topics: Review and Assess the Draft Protocol for Physical/Chemical Testing Review and Assess the Draft Protocol for Life Detection Testing Review and Assess the Draft Protocol for Biohazard Testing Environmental and Health/Monitoring and Safety Issues Requirements of the Draft Protocol for Facilities and Equipment Contingency Planning for Different Outcomes of the Draft Protocol Personnel Management Considerations in Implementation of the Draft Protocol Draft Protocol Implementation Process and Update Concepts This report provides the first complete presentation of the Draft Protocol for Mars Sample Handling to meet planetary protection needs. This Draft Protocol, which was compiled from deliberations and recommendations from earlier Workshops in the Series, represents a consensus that emerged from the discussions of all the sub-groups assembled over the course of the five Workshops of the Series. These discussions converged on a conceptual approach to sample handling, as well as on specific analytical requirements. Discussions also identified important issues requiring attention, as well as research and development needed for protocol implementation

Intelligent Deception Detection through Machine Based Interviewing

In this paper an automatic deception detection system, which analyses participant deception risk scores from non-verbal behaviour captured during an interview conducted by an Avatar, is demonstrated. The system is built on a configuration of artificial neural networks, which are used to detect facial objects and extract non-verbal behaviour in the form of micro gestures over short periods of time. A set of empirical experiments was conducted based a typical airport security scenario of packing a suitcase. Data was collected through 30 participants participating in either a truthful or deceptive scenarios being interviewed by a machine based border guard Avatar. Promising results were achieved using raw unprocessed data on un-optimized classifier neural networks. These indicate that a machine based interviewing technique can elicit non-verbal interviewee behavior, which allows an automatic system to detect deception

On the development of intelligent medical systems for pre-operative anaesthesia assessment

This thesis describes the research and development of a decision support tool for determining a medical patient's suitability for surgical anaesthesia. At present, there is a change in the way that patients are clinically assessedp rior to surgery. The pre-operative assessment, usually conducted by a qualified anaesthetist, is being more frequently performed by nursing grade staff. The pre-operative assessmenet xists to minimise the risk of surgical complications for the patient. Nursing grade staff are often not as experienced as qualified anaesthetists, and thus are not as well suited to the role of performing the pre-operative assessment. This research project used data collected during pre-operative assessments to develop a decision support tool that would assist the nurse (or anaesthetist) in determining whether a patient is suitable for surgical anaesthesia. The three main objectives are: firstly, to research and develop an automated intelligent systems technique for classifying heart and lung sounds and hence identifying cardio-respiratory pathology. Secondly, to research and develop an automated intelligent systems technique for assessing the patient's blood oxygen level and pulse waveform. Finally, to develop a decision support tool that would combine the assessmentsa bove in forming a decision as to whether the patient is suitable for surgical anaesthesia. Clinical data were collected from hospital outpatient departments and recorded alongside the diagnoses made by a qualified anaesthetist. Heart and lung sounds were collected using an electronic stethoscope. Using this data two ensembles of artificial neural networks were trained to classify the different heart and lung sounds into different pathology groups. Classification accuracies up to 99.77% for the heart sounds, and 100% for the lung sounds has been obtained. Oxygen saturation and pulse waveform measurements were recorded using a pulse oximeter. Using this data an artificial neural network was trained to discriminate between normal and abnormal pulse waveforms. A discrimination accuracy of 98% has been obtained from the system. A fuzzy inference system was generated to classify the patient's blood oxygen level as being either an inhibiting or non-inhibiting factor in their suitability for surgical anaesthesia. When tested the system successfully classified 100% of the test dataset. A decision support tool, applying the genetic programming evolutionary technique to a fuzzy classification system was created. The decision support tool combined the results from the heart sound, lung sound and pulse oximetry classifiers in determining whether a patient was suitable for surgical anaesthesia. The evolved fuzzy system attained a classification accuracy of 91.79%. The principal conclusion from this thesis is that intelligent systems, such as artificial neural networks, genetic programming, and fuzzy inference systems, can be successfully applied to the creation of medical decision support tools.EThOS - Electronic Theses Online ServiceMedicdirect.co.uk Ltd.GBUnited Kingdo

On the Recognition of Emotion from Physiological Data

This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure

Affective Game Computing: A Survey

This paper surveys the current state of the art in affective computing principles, methods and tools as applied to games. We review this emerging field, namely affective game computing, through the lens of the four core phases of the affective loop: game affect elicitation, game affect sensing, game affect detection and game affect adaptation. In addition, we provide a taxonomy of terms, methods and approaches used across the four phases of the affective game loop and situate the field within this taxonomy. We continue with a comprehensive review of available affect data collection methods with regards to gaming interfaces, sensors, annotation protocols, and available corpora. The paper concludes with a discussion on the current limitations of affective game computing and our vision for the most promising future research directions in the field

Safety related object and pattern recognition for autonomous machines

Automation is nowadays everywhere, from automated machines in factories that build goods, to robots cleaning your own house. Everyday new ideas about automation come out, trying to make life easier for every person and worker. Work automation is the process where production tasks, that are usually performed by humans, are transferred to some sort of technological element. This change normally comes with optimization of the use of resources, as energy and materials, besides incrementing the quality and precision of the product, while reducing the time needed to make it. This project's goal is to achieve safety measures during the positioning of a vehicle handling cargo, in order to pick up a demountable. To perform such process, several devices are used, a 3D sensor that calculates shape and distance of the objects in front of it using time of flight measurements and a video camera, it will be used to perform the search of the objects, for example, pedestrian detection in this project, in order to give the extra information needed for the software to adapt the movement of the vehicle until its position is the required one to hook the cargo and load it on the vehicle without taking any risk